Statistical Learning Methods for Neuroimaging Data Analysis with Applications

Abstract

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Interplay of Sports and Nutrition in Neurological Health and Recovery

“…Neuroimaging studies present a non-invasive technology and are essential in the understanding of certain pathologies, as well as their progress. It is a technology that stimulates and identifies changes in the direction of the proton rotation axis that appear in the water that makes up the tissues of the organism [62,63]. Briefly, MRI uses magnets with a large magnetic field that drives the protons in the body to align with the field produced [64].…”

NRAAF: A Framework for Comparative Analysis of fMRI Registration Algorithms and Their Impact on Resting-State Neuroimaging Accuracy

“…This deviation from normality necessitates the application of specialised statistical methods that do not rely on normal distribution assumptions, such as non-parametric tests or bootstrap methods. These approaches can provide more accurate interpretations of neuroimaging data, especially when exploring complex neural networks or conducting comparative algorithmic analyses [107].…”

FPLS-DC: functional partial least squares through distance covariance for imaging genetics

“…Furthermore, a joint analysis of comprehensive organ-wide imaging and genetic data not only aids in deciphering the genetic architectures behind organ structure and function ( Bearden and Thompson 2017 , Elliott et al 2018 , Zhao et al 2021 , 2023 ), but also helps in detecting pertinent genetic markers associated with various organ-related disorders, like Osteoarthritis ( Le and Stein 2019 , Wilkinson and Zeggini 2021 ). Over time, this extensive collection of data could pave the way for mapping potential biological pathways that link genetics to imaging endophenotypes for different organs, such as the brain and heart, and relate these to clinical outcomes that are confounded with health factors.Nevertheless, the joint analysis of imaging and genetic data poses considerable challenges to existing statistical methods ( Liu and Calhoun 2014 , Shen and Thompson 2020 , Zhu et al 2023 ). As elucidated by Vounou et al (2010) , methods used in imaging genetics can be classified into four categories: candidate phenotype-candidate gene association (CPCGWA), candidate phenotype-genome-wide association (CPGWA), brain-wide candidate gene association (BWCGA), and brain-wide genome-wide association (BWGWA).…”